Pronounced methane cycling in northern lakes coincided with a rapid rise in atmospheric CH <sub>4</sub> during the last deglacial warming
Bibliographic record
Abstract
Atmospheric methane concentration (AMC) surged by ~50% during the last deglaciation, with northern (>30°N) sources accounting for ~40% of the rise. However, hypothesized sources including expanding lakes, peatlands, and destabilized permafrost or hydrates fail to explain this rapid increase. We use biomarkers, isotopes, and radiocarbon data to reconstruct temperature change, methane cycling, and permafrost thaw from a Tibetan thermokarst lake. Radiocarbon evidence and ultradepleted δ 13 C values (−80.3 per mil) of methane-diagnostic lipids indicate intense cycling of ancient (~2500-year-old) methane during the Younger Dryas–Preboreal transition, coeval with the AMC surge and the most rapid warming. By contrast, methane cycling was weak during the Holocene Climatic Optimum despite peak temperatures. These findings imply that anomalously high rates of warming, rather than absolute temperature alone, may play an important role in triggering enhanced paleo-methane cycling. Rapid warming likely intensified emissions from existing northern lakes, fueling the elusive yet clearly amplified northern methane source that contributed to the deglacial abrupt rise in AMC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".